7期

最新文章

• Deep Neural Newsvendor AI翻译
  作者：Han, Jinhui; Hu, Ming; Shen, Guohao
  作者单位：Peking University; University of Toronto; Hong Kong Polytechnic University
  摘要：We consider a data-driven newsvendor problem, where one has access to past demand data and the associated feature information. We solve the problem by estimating the target conditional quantile function using a deep neural network (DNN). The remarkable representational power of DNNs allows our framework to incorporate or approximate various extant data-driven models. We provide theoretical guarantees in terms of excess risk bounds for the DNN solution characterized by the network structure and...
• Deciphering the PCAOB Inspection Process: Evidence and Predictive Insights from Public Data AI翻译
  作者：Aobdia, Daniel; Li, Edward Xuejun; Ramesh, K.; Shen, Min
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Baruch College (CUNY); City University of New York (CUNY) System; Baruch College (CUNY); Rice University; George Mason University
  摘要：Little is known about how the Public Company Accounting Oversight Board (PCAOB) selects audits for inspections. We exploit public data that track PCAOB searches of issuer Securities and Exchange Commission (SEC) filings and the trial transcripts of United States of America v. David Middendorf to provide the first large-sample evidence on confidential PCAOB monitoring activities. We initially validate that PCAOB searches spike during actual inspections of triennially inspected auditors and for ...
• Correlation Improves Group Testing: Modeling Concentration-Dependent Test Errors AI翻译
  作者：Wan, Jiayue; Zhang, Yujia; Frazier, Peter I.
  作者单位：Cornell University; Cornell University
  摘要：Population-wide screening is a powerful tool for controlling infectious diseases. Group testing can enable such screening despite limited resources. Viral concentration of pooled samples are often positively correlated, either because prevalence and sample collection are influenced by location, or through intentional enhancement via pooling samples according to risk or household. Such correlation is known to improve efficiency when test sensitivity is fixed. However, in reality, a test's sensi...
• Systematic Mispricing of Speculative Stocks and the Cross-Sectional Risk-Return Trade-off AI翻译
  作者：Birru, Justin; Mohrschladt, Hannes; Young, Trevor
  作者单位：University System of Ohio; Ohio State University; University of Potsdam; University of Munster; Tulane University
  摘要：We examine the cross-section of returns from the perspective of a benchmark model that only includes systematic mispricing factors. In contrast to conclusions from standard benchmark models, we recover robust positive risk-return relations for many cross-sectional risk, distress, and friction proxies. Our findings are consistent with systematic mispricing that primarily affects speculative stocks and predominantly results in overpricing, predicting lower returns. Hence, failing to control for ...
• Dynamic Portfolio Selection Under Quantile Maximization AI翻译
  作者：He, Xue Dong; Jiang, Zhaoli; Kou, Steven
  作者单位：Chinese University of Hong Kong; Hong Kong Polytechnic University; Boston University
  摘要：Although maximizing quantiles is intuitively appealing and has an axiomatic foundation, it is difficult to find the optimal portfolio strategy because of time inconsistency. Using an intrapersonal equilibrium approach and focusing on the class of time-varying affine strategies, we find that the only viable outcome is from the median maximization because for other quantiles, either the equilibrium does not exist or there is no investment in risky assets. We also prove that maximizing the median...
• Deep Learning-Based Causal Inference for Large-Scale Combinatorial Experiments: Theory and Empirical Evidence AI翻译
  作者：Ye, Zikun; Zhang, Zhiqi; Zhang, Dennis J.; Zhang, Heng; Zhang, Renyu
  作者单位：University of Washington; University of Washington Seattle; Washington University (WUSTL); Arizona State University; Arizona State University-Tempe; Chinese University of Hong Kong
  摘要：Large-scale online platforms launch hundreds of randomized experiments (also known as A/B tests) every day to iterate their operations and marketing strategies. The combinations of these treatments are typically not exhaustively tested, which triggers an important question of both academic and practical interest. Without observing the outcomes of all treatment combinations, how does one estimate the causal effect of any treatment combination and identify the optimal treatment combination? We d...
• Dynamic Scheduling of a Multiclass Queue in the Halfin-Whitt Regime: A Computational Approach for High-Dimensional Problems AI翻译
  作者：Ata, Baris; Kasikaralar, Ebru
  作者单位：University of Chicago
  摘要：We consider a multiclass queueing model of a telephone call center in which a system manager dynamically allocates available servers to customer calls. Calls can terminate through either service completion or customer abandonment, and the manager strives to minimize the expected total of holding costs plus abandonment costs over a finite horizon. Focusing on the Halfin-Whitt heavy traffic regime, we derive an approximating diffusion control problem and, building on earlier work by Beck et al. ...
• Model Aggregation for Risk Evaluation and Robust Optimization AI翻译
  作者：Mao, Tiantian; Wang, Ruodu; Wu, Qinyu
  作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Waterloo
  摘要：We introduce a new approach for prudent risk evaluation based on stochastic dominance, and it is called the model aggregation (MA) approach. In contrast to the classic worst case risk (WR) approach, the MA approach produces not only a robust value of risk evaluation but also a robust distributional model, independent of any specific risk measure. The MA risk evaluation can be computed through explicit formulas in the lattice theory of stochastic dominance, and under some standard assumptions, ...
• Value Premium and Equity Term Structures of Value and Growth Firms AI翻译
  作者：Hasler, Michael; Khapko, Mariana; Marfe, Roberto
  作者单位：University of Neuchatel; University of Toronto; University Toronto Scarborough; University of Toronto; University of Turin; Collegio Carlo Alberto; University of Turin
  摘要：This paper studies the impact of information processing and learning about expected future cashflows on the level and timing of risk premiums in the cross section of firms. Learning with information sources of different qualities endogenously generates value firms, growth firms, and a value premium in equilibrium. Furthermore, the learning model predicts an upward-sloping equity term structure for value firms and a flat equity term structure for growth firms. Using earnings, return, and news d...
• Social Media Sponsorship: Metrics and Strategies for Finding the Right Content Creator-Sponsor Matches AI翻译
  作者：Doosti, Shahryar; Lee, Stephanie; Tan, Yong
  作者单位：Chapman University System; Chapman University; University of Washington; University of Washington Seattle
  摘要：Social media video sponsorship, where sponsors partner with content creators to promote their brands, has become increasingly popular. Leveraging rich data on sponsored and nonsponsored videos on Facebook, we introduce two metrics, Content Similarity and Audience Closeness, to help sponsors find effective creator-sponsor matches. Content Similarity captures the thematic alignment between a creator's content and a sponsor's brand, whereas Audience Closeness measures how closely aligned a creato...